Modality Bias Calibration Network via Information Disentanglement for Visible–Infrared Person Reidentification

Visible–infrared person reidentification (VI-ReID) in social surveillance systems involves analyzing social behavior using nonoverlapping cross-modality camera sets. It often has poor retrieval performance under modality gap. One way to alleviate such the modality discrepancy is to learn shared person features that are generalizable across different modalities. However, because of significant differences in color between the visible and infrared images, the learned share features are always inclined to specific information of corresponding modality. To this end, we propose a modality bias calibration network (MBCNet) that filters out identity-irrelevant interference and recalibrates the learned modality-shared features. Specifically, to emphasize the modality-shared cues, we employ a feature decomposition module in the feature-level to filter out style variations and extract identity-relevant discriminative cues from the residual feature. In order to achieve a better disentanglement, a dual ranking entropy constraint is further proposed to ensure that the learned features contain only identity-relevant information and discard style-relevant information. Simultaneously, we design a decorrelated orthogonality Loss to ensure the disentangled features are not correlated with each other. Through comprehensive experiments, we demonstrate that MBCNet significantly improves the cross-modality retrieval performance in social surveillance systems and effectively addresses the modality bias training issue.